• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.979-987
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• 2021

Multiple Sclerosis (MS) can be early diagnosed by detecting lesions in brain magnetic resonance images (MRI). Unsupervised anomaly detection methods based on autoencoder have been recently proposed for automated detection of MS lesions. However, these autoencoder-based methods were developed only for 2D images (e.g. 2D cross-sectional slices) of MRI, so do not utilize the full 3D information of MRI. In this paper, therefore, we propose a novel 3D autoencoder-based framework for detection of the lesion volume of MS in MRI. We first define a 3D convolutional neural network (CNN) for full MRI volumes, and build each encoder and decoder layer of the 3D autoencoder based on 3D CNN. We also add a skip connection between the encoder and decoder layer for effective data reconstruction. In the experimental results, we compare the 3D autoencoder-based method with the 2D autoencoder models using the training datasets of 80 healthy subjects from the Human Connectome Project (HCP) and the testing datasets of 25 MS patients from the Longitudinal multiple sclerosis lesion segmentation challenge, and show that the proposed method achieves superior performance in prediction of MS lesion by up to 15%.

• Journal of Convergence for Information Technology
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• v.12 no.1
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• pp.31-38
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• 2022

This paper applies an expert independent unsupervised neural network learning-based multivariate time series data analysis model, MSCRED(Multi-Scale Convolutional Recurrent Encoder-Decoder), and to overcome the limitation, because the MCRED is based on Auto-encoder model, that train data must not to be contaminated, by using learning data sampling technique, called Subset Sampling Validation. By using the vibration data of power plant equipment that has been labeled, the classification performance of MSCRED is evaluated with the Anomaly Score in many cases, 1) the abnormal data is mixed with the training data 2) when the abnormal data is removed from the training data in case 1. Through this, this paper presents an expert-independent anomaly diagnosis framework that is strong against error data, and presents a concise and accurate solution in various fields of multivariate time series data.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.159-166
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• 2022

Artificial Intelligence has stimulated every aspect of today's life. Human thinking quality is trying to be involved through digital tools in all research areas of the modern era. The education industry is also leveraging artificial intelligence magical power. Uses of digital technologies in pedagogical paradigms are being observed from the last century. The widespread involvement of artificial intelligence starts reshaping the educational landscape. Adaptive learning is an emerging pedagogical technique that uses computer-based algorithms, tools, and technologies for the learning process. These intelligent practices help at each learning curve stage, from content development to student's exam evaluation. The quality of information technology students and professionals training has also improved drastically with the involvement of artificial intelligence systems. In this paper, we will investigate adopted digital methods in the education sector so far. We will focus on intelligent techniques adopted for information technology students and professionals. Our literature review works on our proposed framework that entails four categories. These categories are communication between teacher and student, improved content design for computing course, evaluation of student's performance and intelligent agent. Our research will present the role of artificial intelligence in reshaping the educational process.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.406-412
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• 2022

Storing large amounts of data has always been a big problem from the beginning of computing history. Big Data has made huge advancements in improving business processes by finding the customers' needs using prediction models based on web and social media search. The main purpose of big data stream processing frameworks is to allow programmers to directly query the continuous stream without dealing with the lower-level mechanisms. In other words, programmers write the code to process streams using these runtime libraries (also called Stream Processing Engines). This is achieved by taking large volumes of data and analyzing them using Big Data frameworks. Streaming platforms are an emerging technology that deals with continuous streams of data. There are several streaming platforms of Big Data freely available on the Internet. However, selecting the most appropriate one is not easy for programmers. In this paper, we present a detailed description of two of the state-of-the-art and most popular streaming frameworks: Apache Ignite and Hazelcast. In addition, the performance of these frameworks is compared using selected attributes. Different types of databases are used in common to store the data. To process the data in real-time continuously, data streaming technologies are developed. With the development of today's large-scale distributed applications handling tons of data, these databases are not viable. Consequently, Big Data is introduced to store, process, and analyze data at a fast speed and also to deal with big users and data growth day by day.

• The Journal of the Korea Contents Association
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• v.22 no.9
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• pp.614-627
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• 2022

The purpose of this study was to explore the various data and methods employed by HR analytics studies. The researchers selected 78 KCI-indexed empirical articles on HR analytics and categorized them using the Employee Life Cycle framework. This yielded several important findings. First, employee retention has been the most common subject of extant studies, followed by performance management. Second, HR analytics studies have used a variety of data (structured and unstructured) according to their research questions, and the data sources have ranged from organizations' internal systems to national databases. Third, most domestic HR analytics studies have been descriptive and diagnostic, whereas predictive and prescriptive studies have been rare. These results have important theoretical and practical implications for future HR analytics research.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.275-287
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• 2022

As a result, for the increasing expansion by the university faculties in the field of postgraduate studies, The Deanship of Graduate Studies at the university has been established in 1430 AH/2009 CE to specifically address the needs of the current and prospective graduate population to supervise postgraduate studies programs in coordination with the concerned faculties. This comes as a result for the university being certain of the importance of providing postgraduate studies opportunities that follow the bachelor's degree to qualify our ambitious youth appropriately. The University offers 72 different Graduate programs, awarding doctoral and master's degrees along with fellowships and diplomas in various disciplines like health, engineering, science, literary, and educational. Currently, the financial model for admission and students' payment is manual and paper based. This paper proposes to provide a user interface for Financial Management in Deanship of Graduate studies The basic purpose of the system was to minimize human interference and reduce mistakes placed by human interference, also to have efficient and a fast performance, and perform Electronic Data Interchange (EDI) for various tasks such as billing and scheduling details.

• Journal of the Korea Convergence Society
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• v.13 no.2
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• pp.249-255
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• 2022

This study proposed an analysis framework for real-time prediction of CNC processing defects using machine learning-based models that are recently attracting attention as processing defect prediction methods, and applied it to CNC machines. Analysis shows that the XGBoost, CatBoost, and LightGBM models have the same best accuracy, precision, recall, F1 score, and AUC, of which the LightGBM model took the shortest execution time. This short run time has practical advantages such as reducing actual system deployment costs, reducing the probability of CNC machine damage due to rapid prediction of defects, and increasing overall CNC machine utilization, confirming that the LightGBM model is the most effective machine learning model for CNC machines with only basic sensors installed. In addition, it was confirmed that classification performance was maximized when an ensemble model consisting of LightGBM, ExtraTrees, k-Nearest Neighbors, and logistic regression models was applied in situations where there are no restrictions on execution time and computing power.

• Geomechanics and Engineering
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• v.28 no.1
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• pp.11-23
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• 2022

The response of pile foundations under lateral loads are usually analyzed using beam-on-nonlinear-Winkler-foundation (BNWF) model framework employing various forms of empirically derived p-y curves and p-multipliers. In practice, the p-y curve presented by the American Petroleum Institute (API) is most often utilized for piles in granular soils, although its shortcomings are recognized. The objective of this study is to evaluate the performance of the BNWF model and to quantify the error in the estimated pile response compared to a rigorous numerical model. BNWF analyses are performed using three sets of p-y curves to evaluate reliability of the procedure. The BNWF model outputs are compared with results of 3D nonlinear finite element (FE) analysis, which are validated via field load test measurements. The BNWF model using API p-y curve produces higher load-displacement curve and peak bending moment compared with the results of the FE model, because empirical p-y curve overestimates the stiffness and underestimates ultimate resistance up to a depth equivalent to four times the pile diameter. The BNWF model overestimates the peak bending moment by approximately 20-30% using both the API and Reese curves. The p-multipliers are revealed to be sensitive on the p-y curve used as input. These results highlight a need to develop updated p-y curves and p-multipliers for improved prediction of the pile response under lateral loading.

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.13-18
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• 2022

In this paper, we proposed VW-FedAVG(Validation based Weighted FedAVG) which updates the global model by weighting according to performance verification from the models of each device participating in the training. The first method is designed to validate each local client model through validation dataset before updating the global model with a server side validation structure. The second is a client-side validation structure, which is designed in such a way that the validation data set is evenly distributed to each client and the global model is after validation. MNIST, CIFAR-10 is used, and the IID, Non-IID distribution for image classification obtained higher accuracy than previous studies.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.1009-1020
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• 1996

Direct-seeded rice can have comparable yield with transplanted rice if its inherent problems can be solved. It is a labor-saving technology and can significantly reduce production cost because seedling nursery , pulling , and transplanting are omitted. Turnaround time between cropping is reduced hence the possibility of a third annual crop. But direct-seeded rice is very vulnerable to pest attack (by birds, rats, and golden snails), desiccation, weed infestation, and prone to lodging resulting to unstable crop establishment and inconsistent yield. These problems can be solved by anaerobic seeding (sowing pre-germinated seeds under the soil). It requires precise seed placement into the soil to optimize its benefits. We developed a four-row anaerobic direct seeder (US $ 200 commercial price) for this purpose . It consist of a structural framework mounted with a drum -hopper metering device, flotation type drivewheels, spring-loaded and adjustable furrow closers, and furrow open rs, and a plastic rainguard. It can sow in line pre-germinated seeds into the soil thus permitting the use of mechanical weeders for a chemical-free weed control. Its performance was comparable with the Japanese two-row anaerobic seeder (costing US$400) in terms of seed placement and crop establishment. It was tested with five cultivars. Seeding rate varied from 38 kg/ha to 80kg/ha. Crop establishment ranged from 64 to 99 percent while grain yield varied from 3.0 t/ha to 5.4t/ha. A six-row anaerobic seeder was also developed and adapted to a powertiller for increased capacity , field efficiency , and easier operation. The anaerobic seeder is useful to farmers shifting to direct seeding to reduce rice production cost and to researchers conducting agronomic studies in direct-seeded rice. Blueprint of the machine is available free of charge from IRRI.